SoroushVahidi/scidocs huggingface dataset:
World Knowledge as Indirect Supervision for Document Clustering
(2016)

One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then, the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this article, we provide an example of using world knowledge for domain-dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then, we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.
 A preliminary version of this work appeared in the proceedings of KDD 2015 [Wang et al. 2015a]. This journal version has made several major improvements. First, we have proposed a new and general learning framework for machine learning with world knowledge as indirect supervision, where document clustering is a special case in the original paper. Second, in order to make our unsupervised semantic parsing method more understandable, we add several real cases from the original sentences to the resulting logic forms with all the necessary information. Third, we add details of the three semantic filtering methods and conduct deep analysis of the three semantic filters, by using case studies to show why the conceptualization-based semantic filter can produce more accurate indirect supervision. Finally, in addition to the experiment on 20 newsgroup data and Freebase, we have extended the experiments on clustering results by using all the combinations of text (20 newsgroup, MCAT, CCAT, ECAT) and world knowledge sources (Freebase, YAGO2).
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
(2018)

Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain. However, in many real-world settings one seeks to adapt to multiple, but somewhat similar, target domains. Applying pairwise adaptation approaches to this setting may be suboptimal, as they fail to leverage shared information among multiple domains. In this work we propose an information theoretic approach for domain adaptation in the novel context of multiple target domains with unlabeled instances and one source domain with labeled instances. Our model aims to find a shared latent space common to all domains, while simultaneously accounting for the remaining private, domainspecific factors. Disentanglement of shared and private information is accomplished using a unified information-theoretic approach, which also serves to establish a stronger link between the latent representations and the observed data. The resulting model, accompanied by an efficient optimization algorithm, allows simultaneous adaptation from a single source to multiple target domains. We test our approach on three challenging publicly-available datasets, showing that it outperforms several popular domain adaptation methods.
An application of neural net chips: handwritten digit recognition
(1988)

A general-purpose, fully interconnected neural-net chip was used to perform computationally intensive tasks for handwritten digit recognition. The chip has nearly 3000 programmable connections, which can be set for template matching. The templates can be reprogrammed as needed during the recognition sequence. The recognition process proceeds in four major steps. First, the image is captured using a TV camera and a digital framegrab. This image is converted, using a digital computer, to either black or white pixels and scaled to fill a 16*16-pixel frame. Next, using the neural-net chip, the image is skeletonized, i.e. the image is thinned to a backbone one pixel wide. Then, the chip is programmed, and a feature map is created by template-matching stored primitive patterns on the chip with regions on the skeletonized image. Finally, recognition, based on the feature map, is achieved using any one of a variety of statistical and heuristic techniques on a digital computer. Best scores range between 90% and 99% correct classification, depending on the quality of the original handwritten digits.<<ETX>>
FontMatcher: Font Image Paring for Harmonious Digital Graphic Design
(2018)

One of the important aspects in graphic design is choosing the font of the caption that matches aesthetically the associated image. To obtain a good match, users would exhaustively examine a long font list requiring them a substantial effort. This paper presents FontMatcher, which supports users to design digital graphic works harmoniously pairing fonts with an image. The system provides three features, recommendation, explaination and feedback. If a warm feeling image is given as input, the system recommends warm feeling fonts, and then explains what is the distinguishing features of the recommendation, e.g. a cursive shape. Users can also provide feedback to find fonts which correspond to their intention. Our evaluation results show that the recommended fonts scored better than selected fonts by novices and provides competing results with the ones chosen by experienced graphic designers. The system also provides explanations that help increasing the reliability of the recommended results.
A Low-Profile Third-Order Bandpass Frequency Selective Surface
(2009)

We demonstrate a new class of low-profile frequency selective surfaces (FSS) with an overall thickness of lambda/24 and a third-order bandpass frequency response. The proposed FSS is a three layer structure composed of three metal layers, separated by two electrically thin dielectric substrates. Each layer is a two-dimensional periodic structure with sub-wavelength unit cell dimensions and periodicity. The unit cell of the proposed FSS is composed of a combination of resonant and non-resonant elements. It is shown that this arrangement acts as a spatial bandpass filter with a third-order bandpass response. However, unlike traditional third-order bandpass FSSs, which are usually obtained by cascading three identical first-order bandpass FSSs a quarter wavelength apart from one another and have thicknesses in the order of lambda/2 , the proposed structure has an extremely low profile and an overall thickness of about lambda/24 , making it an attractive choice for conformal FSS applications. As a result of the miniaturized unit cells and the extremely small overall thickness of the structure, the proposed FSS has a reduced sensitivity to the angle of incidence of the EM wave compared to traditional third-order frequency selective surfaces. The principles of operation along with guidelines for the design of the proposed FSS are presented in this paper. A prototype of the proposed third-order bandpass FSS is also fabricated and tested using a free space measurement system at C band.
A Coplanar Reconfigurable Folded Slot Antenna Without Bias Network for WLAN Applications
(2009)

The design of a reconfigurable single folded slot antenna is shown. Metal strips are used in the slot to manipulate the ground size around the slot, which yields to changing the slot's perimeter and thus changing the resonant frequency of the antenna. The design of a single folded slot antenna is first illustrated. Later, the design is modified to be reconfigurable. The resonant frequencies for the reconfigurable design are chosen to be applicable with the WLAN applications. The antenna design, simulation, and measurements are described. The simulated results both for the return loss and maximum gain agree with the measurements. The antenna has similar radiation patterns in both bands. The advantage of this design is that the length of the bias lines does not affect the antenna performance, thus making its design, feeding, and matching an extremely simple and low-cost procedure for antenna designers.
An electromechanical interpretation of electrowetting
(2005)

Electrowetting on dielectric-coated electrodes involves two independently observable phenomena: (i) the well-known voltage dependence of the apparent contact angle and (ii) a central electromechanical force that can be exploited to move and manipulate small liquid volumes on a substrate. The electromechanical force does not depend upon field-mediated changes of the contact angle; it is operative even if the liquid surface is constrained. Forces on liquid volumes may be determined using capacitance or the Maxwell stress tensor with no reference made to liquid surface profiles. According to this interpretation, a nonlinear mechanism manifesting a voltage threshold is responsible for both contact angle saturation and the observed clamping of the electromechanical force.
Java-based graphical user interface for MRUI, a software package for quantitation of in vivo/medical magnetic resonance spectroscopy signals
(2001)

This article describes a Java-based graphical user interface for the magnetic resonance user interface (MRUI) quantitation package. This package allows MR spectroscopists to easily perform time-domain analysis of in vivo/medical MR spectroscopy data. We have found that the Java programming language is very well suited for developing highly interactive graphical software applications such as the MRUI system. We also have established that MR quantitation algorithms, programmed in the past in other languages, can easily be embedded into the Java-based MRUI by using the Java native interface (JNI).
Improving Gait Cryptosystem Security Using Gray Code Quantization and Linear Discriminant Analysis
(2017)

Gait has been considered as an efficient biometric trait for user authentication. Although there are some studies that address the task of securing gait templates/models in gait-based authentication systems, they do not take into account the low discriminability and high variation of gait data which significantly affects the security and practicality of the proposed systems. In this paper, we focus on addressing the aforementioned deficiencies in inertial-sensor based gait cryptosystem. Specifically, we leverage Linear Discrimination Analysis to enhance the discrimination of gait templates, and Gray code quantization to extract high discriminative and stable binary template. The experimental results on 38 different users showed that our proposed method significantly improve the performance and security of the gait cryptosystem. In particular, we achieved the False Acceptant Rate of 6×10−5% (i.e., 1 fail in 16983 trials) and False Rejection Rate of 9.2% with 148-bit security.
Optimal Reconstruction with a Small Number of Views
(2017)

Estimating positions of world points from features observed in images is a key problem in 3D reconstruction, image mosaicking, simultaneous localization and mapping and structure from motion. We consider a special instance in which there is a dominant ground plane G viewed from a parallel viewing plane S above it. Such instances commonly arise, for example, in aerial photography. Consider a world point g ∈ G and its worst case reconstruction uncertainty ε(g,S) obtained by merging all possible views of g chosen from S. We first show that one can pick two views sp and sq such that the uncertainty ε(g, {sp, sq}) obtained using only these two views is almost as good as (i.e. within a small constant factor of) ε(g,S). Next, we extend the result to the entire ground plane G and show that one can pick a small subset of S ′ ⊆ S (which grows only linearly with the area of G) and still obtain a constant factor approximation, for every point g ∈ G, to the minimum worst case estimate obtained by merging all views in S. Our results provide a view selection mechanism with provable performance guarantees which can drastically increase the speed of scene reconstruction algorithms. In addition to theoretical results, we demonstrate their effectiveness in an application where aerial imagery is used for monitoring farms and orchards.
Fast Ego-motion Estimation with Multi-rate Fusion of Inertial and Vision
(2007)

This paper presents a tracking system for ego-motion estimation which fuses vision and inertial measurements using EKF and UKF (Extended and Unscented Kalman Filters), where a comparison of their performance has been done. It also considers the multi-rate nature of the sensors: inertial sensing is sampled at a fast sampling frequency while the sampling frequency of vision is lower. the proposed approach uses a constant linear acceleration model and constant angular velocity model based on quaternions, which yields a non-linear model for states and a linear model in measurement equations. Results show that a significant improvement is obtained on the estimation when fusing both measurements with respect to just vision or just inertial measurements. It is also shown that the proposed system can estimate fast-motions even when vision system fails. Moreover, a study of the influence of the noise covariance is also performed, which aims to select their appropriate values at the tuning process. The setup is an end-effector mounted camera, which allow us to pre-define basic rotational and translational motions for validating results. KEY WORDS—vision and inertial, multi-rate systems, sensor fusion, tracking
Weaknesses in the Key Scheduling Algorithm of RC4
(2001)

In this paper we present several weaknesses in the key scheduling algorithm of RC4, and describe their cryptanalytic signi cance. We identify a large number of weak keys, in which knowledge of a small number of key bits su ces to determine many state and output bits with non-negligible probability. We use these weak keys to construct new distinguishers for RC4, and to mount related key attacks with practical complexities. Finally, we show that RC4 is completely insecure in a common mode of operation which is used in the widely deployed Wired Equivalent Privacy protocol (WEP, which is part of the 802.11 standard), in which a xed secret key is concatenated with known IV modi ers in order to encrypt di erent messages. Our new passive ciphertext-only attack on this mode can recover an arbitrarily long key in a negligible amount of time which grows only linearly with its size, both for 24 and 128 bit IV modi ers.
Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering
(2009)

Criminal elements in today’s technology-driven society are using every means available at their disposal to launder the proceeds from their illegal activities. In response, international anti-money laundering (AML) efforts are being made. The events of September 11, 2001, highlighted the need for more sophisticated AML and anti-terrorist financing programs across the industry and nation. In the wake of this, regulators are focusing on the role that technology can play in compliance with laws and ultimately in law enforcement. Banks will have to employ or enhance AML tools and technology to satisfy rising regulatory expectations. While many AML solutions have been in place for some time within the banks, they are faced with the challenge of adapting to the ever-changing risks and methods related to money laundering. In order to provide support for AML decisions, we have formulated an AML conceptual model by following Simon [Simon, H. A. (1977). The new science of management decision. Englewood Cliffs, NJ: Prentice-Hall] decision-making process model. Based on this model, a novel and open multi-agent AML system prototype has been designed and developed. Intelligent agents with their properties of autonomy, reactivity, and proactivity are well suited for dynamic, ill-structured, and complex ML prevention controls. The advanced architecture is able to provide more adaptive, intelligent, and flexible solution for AML. This paper is the first attempt at intelligent agent financial application in the AML domain, with a decision-making/problem-solving process model, an innovative agent-based architecture, and a prototype system. 2007 Elsevier Ltd. All rights reserved.
Latent Skill Embedding for Personalized Lesson Sequence Recommendation
(2016)

Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specific assessments. Akin to collaborative filtering for recommender systems, the algorithm does not require students or content to be described by features, but it learns a representation using access traces. We formulate this problem as a regularized maximum-likelihood embedding of students, lessons, and assessments from historical student-content interactions. An empirical evaluation on large-scale data from Knewton, an adaptive learning technology company, shows that this approach predicts assessment results competitively with benchmark models and is able to discriminate between lesson sequences that lead to mastery and failure.
A deep CNN based multi-class classification of Alzheimer's disease using MRI
(2017)

In the recent years, deep learning has gained huge fame in solving problems from various fields including medical image analysis. This work proposes a deep convolutional neural network based pipeline for the diagnosis of Alzheimer's disease and its stages using magnetic resonance imaging (MRI) scans. Alzheimer's disease causes permanent damage to the brain cells associated with memory and thinking skills. The diagnosis of Alzheimer's in elderly people is quite difficult and requires a highly discriminative feature representation for classification due to similar brain patterns and pixel intensities. Deep learning techniques are capable of learning such representations from data. In this paper, a 4-way classifier is implemented to classify Alzheimer's (AD), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and healthy persons. Experiments are performed using ADNI dataset on a high performance graphical processing unit based system and new state-of-the-art results are obtained for multiclass classification of the disease. The proposed technique results in a prediction accuracy of 98.8%, which is a noticeable increase in accuracy as compared to the previous studies and clearly reveals the effectiveness of the proposed method.
Handwriting development, competency, and intervention.
(2007)

Failure to attain handwriting competency during the school-age years often has far-reaching negative effects on both academic success and self-esteem. This complex occupational task has many underlying component skills that may interfere with handwriting performance. Fine motor control, bilateral and visual-motor integration, motor planning, in-hand manipulation, proprioception, visual perception, sustained attention, and sensory awareness of the fingers are some of the component skills identified. Poor handwriting may be related to intrinsic factors, which refer to the child's actual handwriting capabilities, or extrinsic factors which are related to environmental or biomechanical components, or both. It is important that handwriting performance be evaluated using a valid, reliable, standardized tool combined with informal classroom observation and teacher consultation. Studies of handwriting remediation suggest that intervention is effective. There is evidence to indicate that handwriting difficulties do not resolve without intervention and affect between 10 and 30% of school-aged children. Despite the widespread use of computers, legible handwriting remains an important life skill that deserves greater attention from educators and health practitioners.
The formation and function of plant cuticles.
(2013)

The plant cuticle is an extracellular hydrophobic layer that covers the aerial epidermis of all land plants, providing protection against desiccation and external environmental stresses. The past decade has seen considerable progress in assembling models for the biosynthesis of its two major components, the polymer cutin and cuticular waxes. Most recently, two breakthroughs in the long-sought molecular bases of alkane formation and polyester synthesis have allowed construction of nearly complete biosynthetic pathways for both waxes and cutin. Concurrently, a complex regulatory network controlling the synthesis of the cuticle is emerging. It has also become clear that the physiological role of the cuticle extends well beyond its primary function as a transpiration barrier, playing important roles in processes ranging from development to interaction with microbes. Here, we review recent progress in the biochemistry and molecular biology of cuticle synthesis and function and highlight some of the major questions that will drive future research in this field.
Towards a psychographic user model from mobile phone usage
(2011)

Knowing the users' personality can be a strategic advantage for the design of adaptive and personalized user interfaces. In this paper, we present the results of a first trial conducted with the aim of inferring people's personality traits based on their mobile phone call behavior. Initial findings corroborate the efficacy of using call detail records (CDR) and Social Network Analysis (SNA) of the call graph to infer the Big Five personality factors. On-going work includes a large-scale study that shall refine the accuracy of the models with a reduced margin of error.
Integration of Modular Process Units Into Process Control Systems
(2018)

The modularization of process plants is regarded as a promising approach to cope with upcoming requirements in the process industry regarding flexibility. Within the Decentralized Intelligence for Modular Assets Project, a concept has been developed to overcome current deficiencies in implementing modular plants and especially their automation. On one hand, the approach considers a clear separation of engineering efforts into plant-independent module engineering and plant-specific integration engineering. On the other hand, the concept provides a method for the fast integration of a module's automation system into a higher level process control system. To do so, each module provides its process functionality in encapsulated services. The core of the integration method is a file-based description of the module including its services, operating screens, and communication variables. The so-called “module-type package” is currently under standardization by different organizations. This paper presents the general approach as well as current results of the standardization efforts. Furthermore, the results are executed on an application demonstrator to allow a practical assessment. Thus, module engineering and integration engineering, as well as corresponding software tools can be demonstrated.